In a typical cellular network, a plurality of base stations are distributed over a target coverage area. A backhaul network connects the base stations to a central node which may, for example, include a radio network controller (RNC) or base station controller (BSC). Wireless users, for example mobile users, communicate with the base stations through the wireless air interface.
Each base station applies certain resources to the processing and transmission of communication traffic. These resources may include access channels, channel codes, spreading codes, packet pipes, time slots, and forward link power, among other things. Such resources are referred to here as “cell resources.”
It will be appreciated that such cell resources are finite. As a consequence, such resources may be fully committed at high load levels. The possibility that one or more cell resources may be fully committed can limit the maximum traffic load that a base station can handle.
The demand for cell resources varies both geographically and as a function of time. In mobile networks, for example, the mobile users will not, in general, be uniformly distributed over the target coverage area. Instead, some cells will tend to be more heavily populated than others at a given time. At different times, the relatively high concentrations of users might shift to different cells. User mobility also leads to fading effects which cause resource demand to fluctuate over time.
Even without user mobility, resource demand might have geographical and temporal variations. For example, because radio propagation is affected by distance, terrain, and clutter, the demand for certain cell resources depends on the specific locations of the users within the cell. Moreover, even in a fixed network, resource demand may fluctuate over time due to fading effects and temporal variations in call duration or session duration, and in arrival rate.
Because fluctuation in resource demand has many possible causes, it may occur over many timescales. For example, fading may occur on scales of milliseconds to seconds; the statistical behavior of calls and data sessions may create fluctuations on the order of minutes; and in mobile networks, the number of mobile users in a given cell may change with hourly, daily, or seasonal traffic patterns.
To accommodate such fluctuations, wireless networks are typically designed for peak, rather than for average, traffic demand. As a consequence, there will typically be widespread underutilization of available cell resources. Because cell resources are expensive, such underutilization is undesirable.
Several approaches have been tried for increasing the efficiency with which cell resources are utilized. For example, network planning tools are available for taking advantage of propagation models and traffic projections when planning a new or updated network. Some of these tools include algorithms for tuning some of the parameters that define the cell configuration so as to maximize a measure of network performance. Although useful, such planning tools are limited by the accuracy of their models, and by the fact that it takes weeks or more to implement their prescriptions in an actual network.
In other approaches that have been proposed, link measurements or cell measurements are evaluated by a central processor, which determines local settings at individual base stations which will collectively lead to better coverage, capacity, or some other network performance metric. In such an approach, which is a type of dynamic network self-configuration, the adjustments at the individual base stations might affect, for example, antenna tilt, cell power levels, or power levels of selected downlink channels. Although potentially useful, such approaches suffer the disadvantage that they are complex, slow, and expensive to implement. See, e.g., K. Takeo and S. Sato, “Evaluation of a CDMA cell design algorithm considering non-uniformity of traffic and base station locations,” IEICE Trans. Fundamentals, Vol. E81-A, No. 7, 1998, pg. 1367-1377, and L. Du, J. Bigham, and L. Cuthbert, “Towards intelligent geographic load balancing for mobile cellular networks,” IEEE Transactions on Systems, Man and Cybernetics, C33, No4, 2003, pg. 480-491.
In yet other proposals, various base stations behave as cellular automata. The network configures itself through the collective behavior of the cellular automata, each of which adjusts its access area according to the transmit power of a particular beacon channel. Unlike the type of dynamic network self-configuration described above, the method based on cellular automata does not need a centralized processor. However, it suffers from undesirable complexity because of the need for the automata to intercommunicate through the backhaul, and the need for sequential self-sampling by the cells to optimize the power settings. Sequential self-sampling is slow and subject to the formation of system-wide instabilities. See, e.g., L. T. W. Ho, L. G. Samuel, and J. M. Pitts, “Applying emergent self-organizing behavior for the coordination of 4G networks using complexity metrics”, Bell Labs Technical Journal, 8 (1), 2003, pg. 5-25.
Thus, there remains a need for new approaches to network configuration, that can lead to more efficient resource utilization.